Results 61 to 70 of about 479,475 (300)
Design of a New Excitation Controller of Synchronous Generator
Gong-kuang zidonghua, 2011 In view of the problem that initial control signal is defined by experience in traditional excitation controller of synchronous generator based on iterative learning control algorithm, the paper proposed a design scheme of excitation controller of ...
HAO Xiao-hong, SHI Zhen-lei, ZHANG Ping
doaj
PI output feedback control of differential linear repetitive processes [PDF]
, 2008 Repetitive processes are characterized by a series of sweeps, termed passes, through a set of dynamics defined over a finite duration known as the pass length.
Galkowski, K, Rogers, E, Sulikowski, B
core +1 more source
Advanced Engineering Materials, EarlyView.Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley +1 more source
Control Interpretations for First-Order Optimization Methods
, 2017 First-order iterative optimization methods play a fundamental role in large scale optimization and machine learning. This paper presents control interpretations for such optimization methods.
Hu, Bin, Lessard, Laurent
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Spatial Iterative Learning Control: Output Tracking
IFAC-PapersOnLine, 2017 Abstract This paper focuses on tracking spatially repeatable tasks. In addition, these tasks are not necessarily temporally repeatable in the sense that the finite length of the corresponding time interval may change with each repetition. Because of that, the standard Iterative Learning Control (ILC) framework is not directly applicable.
Ljesnjanin, Merid +3 more
openaire +3 more sources
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Iterative Learning Control Combination with Adaptive Sliding Mode Technique for a Hypersonic Vehicle
Xibei Gongye Daxue Xuebao, 2019 Aiming at the complicated nonlinearities, high uncertainties and strong coupling of hypersonic vehicle, a new adaptive iterative learning control method is put forward. The proposed controller combined iterative learning control with sliding mode control.
doaj +1 more source
Goal-Driven Dynamics Learning via Bayesian Optimization
, 2017 Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the
Bansal, Somil +4 more
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Probabilistic Guarantees for Safe Deep Reinforcement Learning
, 2020 Deep reinforcement learning has been successfully applied to many control tasks, but the application of such agents in safety-critical scenarios has been limited due to safety concerns.
E Ohn-Bar +14 more
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A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source